Search by meaning, not keywords
Traditional search matches words; vector search matches meaning. It's the engine behind modern recommendations, semantic search, and retrieval-augmented generation.
How it works, briefly
- Embeddings turn text or images into numeric vectors.
- Similar meanings sit close together in that space.
- The database finds nearest neighbours, fast, at scale.
Where it helps
Smarter internal search, "find similar" features, and grounding LLMs in your own documents. We help choose the right index and keep latency and cost in check.
